Biomedical subjects

Denis Cousineau

Publications and source records attributed to Denis Cousineau.

7 recordsLinked to original sources

PubMed2005

Attentional blink differences between adolescent dyslexic and normal readers.

The goal of this study was to evaluate the possibility that dyslexic individuals require more working memory resources than normal readers to shift attention from stimulus to stimulus. To test this hypothesis, normal and dyslexic adolescents participated in a Rapid Serial Visual Presentation experiment (Raymond, Shapiro, & Arnell, 1992). Surprisingly, the result showed that the participants with dyslexia produced a shallower attentional blink than normal controls. This result may be interpreted as showing differences in the way the two groups encode information in episodic memory. They also fit in a cascade-effect perspective of developmental dyslexia.

Adolescent↗

PubMed2004

Visual-memory search: an integrative perspective.

A large, single-frame, visual-memory search experiment is reported in which memory and display loads of 1, 2, and 4 alphanumeric characters were factorially combined. In addition to the usual Consistent Mapping and Varied Mapping conditions, the experiment also involved a Categorical Varied Mapping condition in which different sets of stimuli switched roles as targets and distractors over trials. The stimuli used in these various mapping conditions were either digits, letters, or digits and letters. Analyses of the response time means obtained early and late in training suggest that the presence of categorical distinctions among the stimuli is the most important determinant of search efficiency. Comparison of the load effects on the response time means and on their standard deviations revealed a fairly constant ratio throughout the experimental conditions, which suggests that similar search processes may have been involved. A feature-based comparison model is indeed shown to account for the response time means obtained after extensive training under just about all training conditions, as well as for the ratios of load effects on means and standard deviations. According to the model, improvement in search efficiency results from a reduction in the number of features considered. The model's performance questions the necessity to postulate qualitative differences between controlled and automatic processing, while the experiment forces a reassessment of the importance of the consistent mapping that underlies dual-process theories.

Discrimination, Psychological↗

PubMed2004

Termination of a visual search with large display size effects.

The ability to locate an object in the visual field is a collaboration of at least three intermingled processes: scanning multiple locations, recognizing the object sought (the target), and ending the search in cases when the target is not found. In this paper, we focus on the termination rule. Using distribution analyses, it is possible to assess the probability of termination conditional on the number of locations examined. The results show that on some trials without target, the participants carried out more comparisons than there are objects in the display; in other conditions, they carried out fewer comparisons than objects. Because there were very few errors, the premature stops were not pure guesses. We present models to account for these findings. The distributions of terminations help determine the slopes of the functions relating response time to set size.

Attention↗

PubMed2004

QMPE: estimating Lognormal, Wald, and Weibull RT distributions with a parameter-dependent lower bound.

We describe and test quantile maximum probability estimator (QMPE), an open-source ANSI Fortran 90 program for response time distribution estimation. QMPE enables users to estimate parameters for the ex-Gaussian and Gumbel (1958) distributions, along with three "shifted" distributions (i.e., distributions with a parameter-dependent lower bound): the Lognormal, Wald, and Weibul distributions. Estimation can be performed using either the standard continuous maximum likelihood (CML) method or quantile maximum probability (QMP; Heathcote & Brown, in press). We review the properties of each distribution and the theoretical evidence showing that CML estimates fail for some cases with shifted distributions, whereas QMP estimates do not. In cases in which CML does not fail, a Monte Carlo investigation showed that QMP estimates were usually as good, and in some cases better, than CML estimates. However, the Monte Carlo study also uncovered problems that can occur with both CML and QMP estimates, particularly when samples are small and skew is low, highlighting the difficulties of estimating distributions with parameter-dependent lower bounds.

Algorithms↗

PubMed2004

Merging race models and adaptive networks: a parallel race network.

This article presents a generalization of race models involving multiple channels. The major contribution of this article is the implementation of a learning rule that enables networks based on such a parallel race model to learn stimulus-response associations. This model is called a parallel race network. Surprisingly, with a two-layer architecture, a parallel race network learns the XOR problem without the benefit of hidden units. The model described here can be seen as a reduction-of-information system (Haider & Frensch, 1996). An emergent property of this model is seriality: In some conditions, responses are performed with a fixed order, although the system is parallel. The mere existence of this supervised network demonstrates that networks can perform cognitive processes without the weighted sum metric that characterizes strength-based networks.

Association↗

PubMed2004

Fitting distributions using maximum likelihood: methods and packages.

The most powerful tests of response time (RT) models often involve the whole shape of the RT distribution, thus avoiding mimicking that can occur at the level of RT means and variances. Nonparametric distribution estimation is, in principle, the most appropriate approach, but such estimators are sometimes difficult to obtain. On the other hand, distribution fitting, given an algebraic function, is both easy and compact. We review the general approach to performing distribution fitting with maximum likelihood (ML) and a method based on quantiles (quantile maximum probability, QMP). We show that QMP has both small bias and good efficiency when used with common distribution functions (the ex-Gaussian, Gumbel, lognormal, Wald, and Weibull distributions). In addition, we review some software packages performing ML (PASTIS, QMPE, DISFIT, and MATHEMATICA) and compare their results. In general, the differences between packages have little influence on the optimal solution found, but the form of the distribution function has: Both the lognormal and the Wald distributions have non-linear dependencies between the parameter estimates that tend to increase the overall bias in parameter recovery and to decrease efficiency. We conclude by laying out a few pointers on how to relate descriptive models of RT to cognitive models of RT. A program that generated the random deviates used in our studies may be downloaded from www.psychonomic.org/archive/.

Humans↗

PubMed2003

Testing curvatures of learning functions on individual trial and block average data.

Many models offer different explanations of learning processes, some of them predicting equal learning rates between conditions. The simplest method by which to assess this equality is to evaluate the curvature parameter for each condition, followed by a statistical test. However, this approach is highly dependent on the fitting procedure, which may come with built-in biases difficult to identify. Averaging the data per block of training would help reduce the noise present in the trial data, but averaging introduces a severe distortion on the curve, which can no longer be fitted by the original function. In this article, we first demonstrate what is the distortion resulting from block averaging. The block average learning function, once known, can be used to extract parameters when the performance is averaged over blocks or sessions. The use of averages eliminates an important part of the noise present in the data and allows good recovery of the learning curve parameters. Equality of curvatures can be tested with a test of linear hypothesis. This method can be performed on trial data or block average data, but it is more powerful with block average data.

Artifacts↗